Abstract
We consider a class of vector nonlinear error correction models
where the transfer function (or loadings) of the stationary relation-
ships is nonlinear. This includes in particular the smooth transition
models.
A general representation theorem is given which establishes the
dynamic properties of the process in terms of stochastic and deter-
ministic trends as well as stationary components. In particular, the
behaviour of the cointegrating relations is described in terms of geo-
metric ergodicity. Despite the fact that no deterministic terms are
included, the process will have both stochastic trends and a linear
trend in general.
Gaussian likelihood-based estimators are considered for the long-
run cointegration parameters, and the short-run parameters. Asymp-
totic theory is provided for these and it is discussed to what extend
asymptotic normality and mixed normaity can be found. A simulation
study reveals that cointegration vectors and the shape of the adjust-
ment are quite accurately estimated by maximum likelihood, while
at the same time there is very little information about some of the
individual parameters entering the adjustment function.
where the transfer function (or loadings) of the stationary relation-
ships is nonlinear. This includes in particular the smooth transition
models.
A general representation theorem is given which establishes the
dynamic properties of the process in terms of stochastic and deter-
ministic trends as well as stationary components. In particular, the
behaviour of the cointegrating relations is described in terms of geo-
metric ergodicity. Despite the fact that no deterministic terms are
included, the process will have both stochastic trends and a linear
trend in general.
Gaussian likelihood-based estimators are considered for the long-
run cointegration parameters, and the short-run parameters. Asymp-
totic theory is provided for these and it is discussed to what extend
asymptotic normality and mixed normaity can be found. A simulation
study reveals that cointegration vectors and the shape of the adjust-
ment are quite accurately estimated by maximum likelihood, while
at the same time there is very little information about some of the
individual parameters entering the adjustment function.
| Originalsprog | Engelsk |
|---|---|
| Udgivelsessted | Aarhus |
| Udgiver | Institut for Økonomi, Aarhus Universitet |
| Antal sider | 44 |
| Status | Udgivet - 2007 |